Robust CFAR Ship Detector Based on Bilateral-Trimmed-Statistics of Complex Ocean Scenes in SAR Imagery: A Closed-Form Solution

A robust constant false alarm rate (RCFAR) detector based on bilateral-trimmed-statistics (BTS-RCFAR) with a closed-form solution is proposed. BTS-RCFAR aims at improving the detection performance in complex ocean scenes such as the multiple-target environment, off-shore, oil-spilled ocean area, etc. In these circumstances, the clutter samples are often contaminated by the outliers. Consequently, the estimated parameters are biased, and the probability density function modeling of the clutter is not accurate. Detection performance deteriorates with either decrease of the detection rate or increase of the false alarm rate. Inspired by Sigma filter, BTS-RCFAR proposes a bilateral-thresholds-based strategy to automatically trim the samples in the local reference window, both the high-intensity and the low-intensity outliers are eliminated. Furthermore, the trimming depth is adaptively derived according to the homogeneity of the clutter backgrounds, where the outliers are completely removed and the real clutter samples can be greatly sustained. Maximum-likelihood-estimator with a closed-form solution is used for parameter estimation using the bilateral-trimmed samples, and log-normal model of the sea clutter can be accurately established. Finally, the test cell is detected given the specified probability of false alarm rate. BTS-RCFAR improves the detection performance in complex ocean scenes by elevating the detection rate and reducing the false alarm rate. Both simulated data and real data are used for validation.